Update app.py

app.py

@@ -43,5 +43,87 @@ def main():
     st.balloons()
     st.stop()
 
+if __name__ == "__main__":
+    main()
+
+
+
+
+# Hard-coded API key for demonstration purposes
+API_KEY = "QR8F9B7T6R2SWTAT"
+
+def fetch_alpha_vantage_data(api_key, symbol):
+    
+    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
+    response = requests.get(url)
+    alpha_vantage_data = response.json()
+    return alpha_vantage_data
+
+def calculate_indicators(data):
+    # Convert all columns to numeric
+    data = data.apply(pd.to_numeric, errors='coerce')
+
+    # Example: Simple condition for doji and inside
+    data['Doji'] = abs(data['Close'] - data['Open']) <= 0.01 * (data['High'] - data['Low'])
+    data['Inside'] = (data['High'] < data['High'].shift(1)) & (data['Low'] > data['Low'].shift(1))
+    return data
+
+def display_signals(signal_type, signals):
+    st.subheader(f"{signal_type} Signals:")
+    st.write(signals)
+
+def main():
+    st.title("Stock Trend Predictor")
+
+    # Input for stock symbol
+    symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")
+
+    # Fetch Alpha Vantage data
+    alpha_vantage_data = fetch_alpha_vantage_data(API_KEY, symbol)
+
+    # Extract relevant data from Alpha Vantage response
+    alpha_vantage_time_series = alpha_vantage_data.get('Time Series (5min)', {})
+    df = pd.DataFrame(alpha_vantage_time_series).T
+    df.index = pd.to_datetime(df.index)
+    df = df.dropna(axis=0)
+
+    # Rename columns
+    df = df.rename(columns={'1. open': 'Open', '2. high': 'High', '3. low': 'Low', '4. close': 'Close', '5. volume': 'Volume'})
+
+    # Calculate indicators
+    df = calculate_indicators(df)
+
+    # Display stock trading signals
+    strategic_signals = StrategicSignals(symbol=symbol)
+
+    # Display loading message during processing
+    with st.spinner("Predicting signals using Strategic Indicators..."):
+        # Display signals
+        st.subheader(":orange[Strategic Indicators Trend Prediction]")
+        display_signals("Bollinger Bands", strategic_signals.get_bollinger_bands_signals())
+        display_signals("Breakout", strategic_signals.get_breakout_signals())
+        display_signals("Crossover", strategic_signals.get_crossover_signals())
+        display_signals("MACD", strategic_signals.get_macd_signals())
+        display_signals("RSI", strategic_signals.get_rsi_signals())
+
+    # Create predictor
+    my_market_predictor = Pandas_Market_Predictor(df)
+
+    # Predict Trend
+    indicators = ["Doji", "Inside"]
+
+    # Display loading message during prediction
+    with st.spinner("Predicting trend using AI ...."):
+        # Predict trend
+        trend = my_market_predictor.Trend_Detection(indicators, 10)
+
+    # Display results
+    st.subheader(":orange[AI Trend Prediction]")
+    st.write("Buy Trend :", trend['BUY'])
+    st.write("Sell Trend :", trend['SELL'])
+
+    # Delete the DataFrame to release memory
+    del df
+
 if __name__ == "__main__":
     main()